Thermal conductivity mapping from rock physics guided seismic inversion

ABSTRACT

Modeling basin geology in a subsurface region includes receiving seismic data representing acoustic signals that are reflected from regions of the subsurface; receiving potential fields data comprising potential field values that are mapped to locations in the subsurface; determining a relationship between the seismic data and the potential field values for each of the locations in the subsurface; generating, based on the relationship for each location, a three-dimensional (3D) map of thermal conductivity in the subsurface region; and based on the 3D map of thermal conductivity, identifying at least one area comprising source rock having a threshold maturity, the threshold maturity indicative of potential hydrocarbons in the subsurface.

TECHNICAL FIELD

The present specification generally relates to an approach for modelingfeatures in a subterranean formation. More specifically, thisspecification describes systems and methods for basin modeling based onestimations of thermal conductivity of rock formations.

BACKGROUND

Hydrocarbons, such as oil and gas, occur in the Earth's subsurface at adepth ranging from a few hundred meters to several kilometers and arefound in geological formations, which are layers of rock. As such,prospecting for hydrocarbons includes the difficult tasks ofidentification of where such geological formations exist and extractionof the hydrocarbons from these geological formations at such depths.Identifying the location of hydrocarbons may include the conducting ofgeophysical surveys collected through, for example, seismic prospecting.These geophysical surveys can be employed to construct geological mapsrepresenting the structure of areas of the outer crust of the Earth.

SUMMARY

The present specification describes systems and processes for modeling abasin in a subsurface. A computing system (e.g., a data processingsystem) is configured to execute a procedure for thermal conductivityestimation. The computing system improves measurements for the parameterof thermal conductivity. Thermal conductivity includes a parameterdescribing a source rock maturity. In an example, thermal conductivityis a rate at which heat is transferred by conduction through a unitcross-section area of a rock material, such as when a temperaturegradient exits perpendicular to the area. Seismic data and thermalconductivity are physically related (e.g., due to porosity of rock inthe subsurface), and can strongly correlate to one another. As a result,the data processing system described in this specification can perform amore accurate estimation of thermal conductivity to generate moreaccurate models of basin geology.

The systems and methods for procedure for thermal conductivityestimation to determine values for elastic attributes of seismic wavesfor three dimensional (3D) areal distribution of seismic data. The dataprocessing system determines properties for both primary waves (P-waves)and secondary waves (S-waves) of the seismic data. P-waves includecompressional waves that are longitudinal in nature. P-waves includepressure waves that travel faster than other waves through the earth toarrive at seismograph stations before S-waves. S-waves include shearwaves that are transverse in nature. S-waves (e.g., generated in aseismic signal) arrive at seismograph stations after the faster-movingP-waves and displace the ground perpendicular to the direction ofpropagation. The elastic properties for the S-waves and P-waves caninclude parameters such as attenuation factor and illustrate the elasticproperties of the rock at the basin, such as the relation betweendeformation and stress defined by the elastic constant(s) of the rock(s)at the basin. The data processing system models the basin geology basedon the estimate of these elastic parameters.

Currently, default values from tables are used as inputs for basinmodeling. Generally, basin modeling uses thermal conductivity values asinputs for predictions of basin structure. The determined elasticparameters from the seismic data provide a more accurate model relativeto using default values from tables, the values being representative ofthe region. For example, the system performs heat flow prediction bycombining thermal conductivity and temperature gradient data usingFourier's Law q=−k∇T, where q is the local heat flux density in Wattsper meter squared, k is the material's conductivity in Watts permeter-Kelvin, and ∇T is the temperature gradient in Kelvins per meter.

Additionally, the processes and systems described in this specificationprovide a three-dimensional (3D) map of thermal conductivity. Themapping is based on a rock physics transform that relates seismicinversion elastic attributes to thermal conductivity. The systems andmethods are configured to provide a visualization of how thermalconductivity varies horizontally and vertically in the subsurface. Thevisualization also shows how thermal conductivity varies in relation tofacies in the subsurface that are predicted from seismic inversionmodels.

The implementations described herein enable one or more of the followingadvantages. The systems and processes described in this specificationprovide a more accurate estimate of thermal conductivity relative tomodels that use default elastic parameter values for seismic data. Forexample, the rock-physics based seismic inversion process described inthis specification bypasses uncertainties that are embedded in aconventional P-wave velocity method that depends on the calculation ofinterval velocities from stacking velocities. The resulting 3D map ofthermal conductivity increases an efficiency of source rock maturityanalysis, resulting in risk mitigation for drilling. For example, a riskthat unproductive wells are drilled is reduced because the improvedbasin models have more accurate source rock thermal maturity values. The3D map enables exploration concentration on areas of mature source rock.Wells are not drilled in areas where the source rock is not matureenough to generate hydrocarbons. Additionally, the 3D map of thermalconductivity provides evidence for basin evolution in the subsurfaceregion. The 3D map of thermal conductivity provides additional dataillustrating the process of hydrocarbon maturation in a region.Furthermore, the 3D map of thermal conductivity illustrates a geothermalcondition of a geologic setting.

Each of these advantages are enabled by one or more of the followingembodiments.

In a general aspect, a method is performed for modeling basin geology ina subsurface region. The method includes receiving seismic datarepresenting acoustic signals that are reflected from regions of thesubsurface region. The method includes receiving potential fields datacomprising potential field values that are mapped to locations in thesubsurface region. The method includes determining a relationshipbetween the seismic data and the potential field values for each of thelocations in the subsurface region. The method includes generating,based on the relationship for each of the locations, a three-dimensional(3D) map of thermal conductivity in the subsurface region. The methodincludes, based on the 3D map of thermal conductivity, identifying atleast one area comprising source rock having a threshold maturity, thethreshold maturity being indicative of potential hydrocarbons in thesubsurface region.

In some implementations, the method includes determining a temperaturecurve in a zone of interest based on the potential field values. In someimplementations, the method includes estimating a temperature gradientfor each layer of a subsurface formation in the subsurface region fromthe temperature curve. In some implementations, the relationship betweenthe seismic data and the potential field values for each of thelocations in the subsurface region is based on the temperature gradientfor a respective layer of subsurface formation.

In some implementations, the method includes determining a thermalconductivity for each layer of the subsurface formation from thetemperature gradient for the respective layer of the subsurfaceformation.

In some implementations, the method includes performing a seismicinversion on the seismic data. In some implementations, the methodincludes generating, based on the seismic inversion, values for one ormore elastic properties of the subsurface region. In someimplementations, the relationship between the seismic data and thepotential field values for each of the locations in the subsurfaceregion comprises a relationship between the thermal conductivity of thesubsurface region and the one or more elastic properties.

In some implementations, the one or more elastic properties compriselithology, porosity, water saturation, permeability and density. In someimplementations, the method includes determining, based on the seismicinversion, an acoustic impedance across the subsurface region. In someimplementations, the method includes generating a rock physics template(RPT) based on relating the acoustic impedance to thermal conductivityin the subsurface region. In some implementations, the method includes,based on the RPT, propagate thermal conductivity values through thesubsurface region to form the 3D map of thermal conductivity.

In some implementations, the potential fields data comprises productionlogging tool (PLT) data, downhole seismic testing (DST) data, bottomhole temperature (BHT) log data, or a combination thereof.

In some implementations, the method includes drilling a well based onidentifying at least one area comprising source rock having a thresholdmaturity.

In a general aspect, a system for modeling basin geology in a subsurfaceregion includes at least one processor at least one memory storinginstructions that, when executed by the at least one processor, causethe at least one processor to perform operations. The operations includereceiving seismic data representing acoustic signals that are reflectedfrom regions of the subsurface region. The operations include receivingpotential fields data comprising potential field values that are mappedto locations in the subsurface region. The operations includedetermining a relationship between the seismic data and the potentialfield values for each of the locations in the subsurface region. Theoperations include generating, based on the relationship for each of thelocations, a three-dimensional (3D) map of thermal conductivity in thesubsurface region. The operations include, based on the 3D map ofthermal conductivity, identifying at least one area comprising sourcerock having a threshold maturity, the threshold maturity beingindicative of potential hydrocarbons in the subsurface region.

In some implementations, the operations include determining atemperature curve in a zone of interest based on the potential fieldvalues. In some implementations, the operations include estimating atemperature gradient for each layer of a subsurface formation in thesubsurface region from the temperature curve. In some implementations,the relationship between the seismic data and the potential field valuesfor each of the locations in the subsurface region is based on thetemperature gradient for a respective layer of subsurface formation.

In some implementations, the operations include determining a thermalconductivity for each layer of the subsurface formation from thetemperature gradient for the respective layer of the subsurfaceformation.

In some implementations, the operations include performing a seismicinversion on the seismic data. In some implementations, the operationsinclude generating, based on the seismic inversion, values for one ormore elastic properties of the subsurface region. In someimplementations, the relationship between the seismic data and thepotential field values for each of the locations in the subsurfaceregion comprises a relationship between the thermal conductivity of thesubsurface region and the one or more elastic properties.

In some implementations, the one or more elastic properties compriselithology, porosity, water saturation, permeability and density. In someimplementations, the operations include determining, based on theseismic inversion, an acoustic impedance across the subsurface region.In some implementations, the operations include generating a rockphysics template (RPT) based on relating the acoustic impedance tothermal conductivity in the subsurface region. In some implementations,the operations include, based on the RPT, propagate thermal conductivityvalues through the subsurface region to form the 3D map of thermalconductivity.

In some implementations, the potential fields data comprises productionlogging tool (PLT) data, downhole seismic testing (DST) data, bottomhole temperature (BHT) log data, or a combination thereof.

In some implementations, the operations include drilling a well based onidentifying at least one area comprising source rock having a thresholdmaturity.

In a general aspect, one or more non-transitory computer-readable mediastore instructions for modeling basin geology in a subsurface region.The instructions, when executed by at least one processor, areconfigured to cause the at least one processor to perform operations.The operations include receiving seismic data representing acousticsignals that are reflected from regions of the subsurface region. Theoperations include receiving potential fields data comprising potentialfield values that are mapped to locations in the subsurface region. Theoperations include determining a relationship between the seismic dataand the potential field values for each of the locations in thesubsurface region. The operations include generating, based on therelationship for each of the locations, a three-dimensional (3D) map ofthermal conductivity in the subsurface region. The operations include,based on the 3D map of thermal conductivity, identifying at least onearea comprising source rock having a threshold maturity, the thresholdmaturity being indicative of potential hydrocarbons in the subsurfaceregion.

In some implementations, the operations include determining atemperature curve in a zone of interest based on the potential fieldvalues. In some implementations, the operations include estimating atemperature gradient for each layer of a subsurface formation in thesubsurface region from the temperature curve. In some implementations,the relationship between the seismic data and the potential field valuesfor each of the locations in the subsurface region is based on thetemperature gradient for a respective layer of subsurface formation.

In some implementations, the operations include determining a thermalconductivity for each layer of the subsurface formation from thetemperature gradient for the respective layer of the subsurfaceformation.

In some implementations, the operations include performing a seismicinversion on the seismic data. In some implementations, the operationsinclude generating, based on the seismic inversion, values for one ormore elastic properties of the subsurface region. In someimplementations, the relationship between the seismic data and thepotential field values for each of the locations in the subsurfaceregion comprises a relationship between the thermal conductivity of thesubsurface region and the one or more elastic properties.

In some implementations, the one or more elastic properties compriselithology, porosity, water saturation, permeability and density. In someimplementations, the operations include determining, based on theseismic inversion, an acoustic impedance across the subsurface region.In some implementations, the operations include generating a rockphysics template (RPT) based on relating the acoustic impedance tothermal conductivity in the subsurface region. In some implementations,the operations include, based on the RPT, propagate thermal conductivityvalues through the subsurface region to form the 3D map of thermalconductivity.

In some implementations, the potential fields data comprises productionlogging tool (PLT) data, downhole seismic testing (DST) data, bottomhole temperature (BHT) log data, or a combination thereof.

In some implementations, the operations include drilling a well based onidentifying at least one area comprising source rock having a thresholdmaturity.

The previously described implementation is implementable using acomputer-implemented method; a non-transitory, computer-readable mediumstoring computer-readable instructions to perform thecomputer-implemented method; and a computer-implemented system includinga computer memory interoperably coupled with a hardware processorconfigured to perform the computer-implemented method/the instructionsstored on the non-transitory, computer-readable medium.

The details of one or more embodiments are set forth in the accompanyingdrawings and the description below. Other features and advantages willbe apparent from the description and drawings, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic view of seismic and potential fields surveys beingperformed to map subterranean features such as facies, faults, andlayers.

FIG. 2 illustrates a three-dimensional cube representing a subterraneanformation.

FIG. 3 illustrates a stratigraphic trace within the three-dimensionalcube of FIG. 2 .

FIG. 4 is a flowchart of an example of a process for determining sourcerock maturity from seismic data and a rock physics transform thatrelates seismic inversion elastic attributes of the seismic data tothermal conductivity in the subsurface.

FIG. 5A illustrates a process for determining relationship betweenseismic data and thermal conductivity in a subsurface region (such asthe 3D cube of FIG. 2 ).

FIG. 5B illustrates a process for determining thermal conductivitywithin a three-dimensional cube (e.g., of FIG. 2 ) from seismic dataacquired in that three-dimensional cube.

FIG. 6 illustrates a system for determining source rock maturity fromseismic data and a rock physics transform that relates seismic inversionelastic attributes of the seismic data to thermal conductivity in thesubsurface.

FIG. 7 is a block diagram illustrating an example computer system usedto provide computational functionalities associated with describedalgorithms, methods, functions, processes, flows, and procedures asdescribed in the present specification, according to someimplementations of the present specification.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

This disclosure describes data processing systems and processes formodeling basin geology in a subsurface region. A computing system (e.g.,a data processing system) receives seismic data from a seismic receiver,as subsequently described. The computing system performs seismicinversion. The computing system estimates thermal conductivity andgenerates a three dimensional map of thermal conductivity in thesubsurface region. Thermal conductivity includes a parameter describinga source rock maturity in the subsurface region. Thermal conductivity isa rate at which heat is transferred by conduction through a unitcross-section area of a rock material, such as when a temperaturegradient exits perpendicular to the area. Seismic data and thermalconductivity are physically related because of porosity of rock in thesubsurface. Velocity values measured in the traces of seismic datacorrelate to thermal conductivity values of the rock. The computingsystem performs a more accurate estimation of thermal conductivity togenerate more accurate models of basin geology, relative to estimates ofthermal conductivity performed using default values that are derivedfrom rock properties of the subsurface region.

The computing system described in this specification uses thermalconductivity as a parameter to estimate source rock maturity. Ratherthan default values from tables as input to basin analysis, thecomputing system is configured to determine values of elastic attributesfrom seismic data. Specifically, the computing system uses a 3D arealdistribution of seismic data, as subsequently described. The computingsystem executes a process that uses each of petrophysics, rock physicsand seismic inversion. The computing system uses petrophysics todetermine formation temperatures using production logging tool (PLT)data, drill stem test (DST) data, and bottom hole temperature (BHT) logdata. The computing system uses these data to determine a temperaturecurve in a zone of interest. The computing system estimates atemperature gradient for each layer of the subsurface formation from thetemperature data. The computing system determines a thermal conductivityfor each layer from the temperature gradient. The computing systemgenerates data representing relationships between thermal conductivityand elastic properties using rock physics transforms (RPT) for thesubsurface region. The computing system then uses 3D seismic data (suchas from seismic inversion) to produce an areal map of thermalconductivity using RPT. The computing system then estimates source rockmaturity from the areal map of thermal conductivity. The source rockmaturity and areal map of thermal conductivity are each used, such asduring hydrocarbon exploration, for drilling risk mitigation. Forexample, areas with older source rock (or more mature source rock) aremore likely to yield hydrocarbon deposits. When rock maturity exceeds aminimum threshold representing a required rock maturity, it is possiblefor that region to include hydrocarbons such as oil or gas. This processis subsequently described in greater detail with respect to the figures.

FIG. 1 is a schematic view of a seismic survey being performed to mapsubterranean features such as facies and faults in a subterraneanformation 100. The seismic survey can provide the underlying basis forimplementation of the systems and methods described herein. Thesubterranean formation 100 includes a layer of impermeable cap rocks 102at the surface. Facies underlying the impermeable cap rocks 102 includea sandstone layer 104, a limestone layer 106, and a sand layer 108. Afault line 110 extends across the sandstone layer 104 and the limestonelayer 106.

Oil and gas tend to rise through permeable reservoir rock until furtherupward migration is blocked, for example, by the layer of impermeablecap rock 102. Seismic surveys attempt to identify locations whereinteraction between layers of the subterranean formation 100 are likelyto trap oil and gas by limiting this upward migration. For example, FIG.1 shows an anticline trap 107, where the layer of impermeable cap rock102 has an upward convex configuration, and a fault trap 109, where thefault line 110 might allow oil and gas to flow along with clay materialbetween the walls traps the petroleum. Other traps include salt domesand stratigraphic traps.

A seismic source 112 (for example, a seismic vibrator or an explosion)generates seismic waves 114 that propagate in the earth. The velocity ofthese seismic waves depends on several properties, for example, density,porosity, and fluid content of the medium through which the seismicwaves are traveling. Different geologic bodies or layers in the earthare distinguishable because the layers have different properties and,thus, different characteristic seismic velocities. For example, in thesubterranean formation 100, the velocity of seismic waves travelingthrough the subterranean formation 100 will be different in thesandstone layer 104, the limestone layer 106, and the sand layer 108. Asthe seismic waves 114 contact interfaces between geologic bodies orlayers that have different velocities, the interfaces reflect some ofthe energy of the seismic wave and refracts some of the energy of theseismic wave. Such interfaces are sometimes referred to as horizons.

The seismic waves 114 are received by a sensor or sensors 116. Althoughillustrated as a single component in FIG. 1 , the sensor or sensors 116are typically a line or an array of sensors 116 that generate outputsignals in response to received seismic waves including waves reflectedby the horizons in the subterranean formation 100. The sensors 116 canbe geophone-receivers that produce electrical output signals transmittedas input data, for example, to a computer 118 on a seismic control truck120. Based on the input data, the computer 118 may generate a seismicdata output, for example, a seismic two-way response time plot.

A control center 122 can be operatively coupled to the seismic controltruck 120 and other data acquisition and wellsite systems. The controlcenter 122 may have computer facilities for receiving, storing,processing, and analyzing data from the seismic control truck 120 andother data acquisition and wellsite systems. For example, computersystems 124 in the control center 122 can be configured to analyze,model, control, optimize, or perform management tasks of fieldoperations associated with development and production of resources suchas oil and gas from the subterranean formation 100. Alternatively, thecomputer systems 124 can be located in a different location than thecontrol center 122. Some computer systems are provided withfunctionality for manipulating and analyzing the data, such asperforming seismic interpretation or borehole resistivity image loginterpretation to identify geological surfaces in the subterraneanformation or performing simulation, planning, and optimization ofproduction operations of the wellsite systems.

In some embodiments, results generated by the computer system 124 may bedisplayed for user viewing using local or remote monitors or otherdisplay units. One approach to analyzing seismic data is to associatethe data with portions of a seismic cube representing the subterraneanformation 100. The seismic cube can also be display results of theanalysis of the seismic data associated with the seismic survey.

A basement sediment interface 123 is mapped by the computer system 124.A magnetic method is one example of a potential fields technique formapping the basement-sediment interface 123. This is because sedimentrocks have low magnetic susceptibility compared to those of the basementrocks. Potential fields surveys can take place on land, airborne sensorssupported by a plane 125, and using marine sensors (not shown). Airborneacquisition of potential fields data is the most common in oil and gasexploration. The resolution depends on flight line spacing and a flightaltitude of the plane 125.

FIG. 2 illustrates a seismic cube 140 representing at least a portion ofthe subterranean formation 100. The seismic cube 140 is composed of anumber of voxels 150. A voxel is a volume element, and each voxelcorresponds, for example, with a seismic sample along a seismic trace.The cubic volume C is composed along intersection axes of offset spacingtimes based on a delta-X offset spacing 152, a delta-Y offset spacing154, and a delta-Z offset spacing 156. Within each voxel 150,statistical analysis can be performed on data assigned to that voxel todetermine, for example, multimodal distributions of travel times andderive robust travel time estimates (according to mean, median, mode,standard deviation, kurtosis, and other suitable statistical accuracyanalytical measures) related to azimuthal sectors allocated to the voxel150.

FIG. 3 illustrates a seismic cube 200 representing a formation. Theseismic cube has a stratum 202 based on a surface (for example,amplitude surface 204) and a stratigraphic horizon 206. The amplitudesurface 204 and the stratigraphic horizon 206 are grids that includemany cells such as exemplary cell 208. Each cell is a seismic tracerepresenting an acoustic wave. Each seismic trace has an x-coordinateand a y-coordinate, and each data point of the trace corresponds to acertain seismic travel time or depth (t or z). For the stratigraphichorizon 206, a time value is determined and then assigned to the cellsfrom the stratum 202. For the amplitude surface 204, the amplitude valueof the seismic trace at the time of the corresponding horizon isassigned to the cell. This assignment process is repeated for all of thecells on this horizon to generate the amplitude surface 204 for thestratum 202. In some instances, the amplitude values of the seismictrace 210 within window 212 by horizon 206 are combined to generate acompound amplitude value for stratum 202. In these instances, thecompound amplitude value can be the arithmetic mean of the positiveamplitudes within the duration of the window, multiplied by the numberof seismic samples in the window.

FIG. 4 is a flowchart of an example of a process 400 for modeling basingeology in a subsurface region. The process 400 can be performed by adata processing system, such as the computing system 700 described inrelation to FIG. 7 , or the computer system 124 described in relation toFIG. 1 . The computing system executes a process 400 that uses each ofpetrophysics, rock physics and seismic inversion.

The computing system receives (402) logging data including PLT log data,DST log data, and BHT log data. A typical resolution of PLT/BHT logs is˜0.5 foot (ft.) or 1 ft. in a wellbore in which the logging isperformed. In some implementations, each of the logs are acquired usinga string of measurement tools that are inserted in the borehole. For DSTlog data, the sensors acquire stationary measurements including arepresentation of a reservoir pressure across a zone of interest in thesubsurface formation. In some implementations, other auxiliarymeasurements can also be performed. In some implementations, theauxiliary measurements include data from a repeat formation tester(RFT). RFT measurements include fluid pressures measured with awireline. A padded tool seals formations and pumps fluid untilresistivity indexes indicate formation fluids are present. The RFTsensors measure temperature and pressure and recovers a fluid sample. Insome implementations, the auxiliary measurements include modularformation dynamics tester (MDT) measurements. MDT measurements includereal-time flowline resistivity measurements at the probe module todiscriminate between formation fluids and filtrate from water- andoil-based muds. Until an acceptably low level of contamination can berecovered, formation fluid is excluded from sample recovery Each of RFTand MDT measurements are used to calibrate the pressure data within theborehole. In an example, LiDAR point data are shown in Table 1 showingtemperature data along with other measurements in a sample borehole.Here, Table 1 shows temperature data for a well in degrees Fahrenheit.

TABLE 1 Temperature Data for Example Well Depth (Feet) Temperature (F.)12001 270.2831 12002 270.2842 12003 270.2859 12004 270.2825 12005270.2909 12006 270.2867 12007 270.2851 12008 270.2804 12009 270.284412010 270.289 12011 270.2916 12012 270.2853 12013 270.2878 12014270.2896 12015 270.2895 12016 270.2862 12017 270.2874 12018 270.2898

As described herein, petrophysics includes physical and chemicalproperties of rocks and their contained fluids. The computing systemuses the logging data described herein to determine the rock propertiesof the reservoir, particularly how pores in the subsurface areinterconnected, and how the pore network controls the accumulation andmigration of hydrocarbons. The property values determined by thecomputing system include lithology, porosity, water saturation,permeability and density. For temperature measurements, most of thetools acquiring well logs measure the borehole temperature as anauxiliary measurement. Alternatively, if the temperature measurements inthe borehole are not available, the computing system performstemperature gradient conversions to estimate borehole temperature. Forexample, a temperature gradient conversion is shown as follows: 1 degreeFahrenheit per 100 feet is equal to 1.823 degrees Celsius per 100meters. Conversely, 1 degree Celsius per 100 meters is 0.5486 degreesFahrenheit per 100 feet.

The computing system uses these data to determine (404) a temperaturecurve in a zone of interest (such as the seismic volume of FIG. 2 ). Thecomputing system estimates (406) a temperature gradient for each layerof the subsurface formation from the temperature curve. The temperaturegradient is determined using Fourier's Law q=−k∇T, where q is the localheat flux density in Watts per meter squared, k is the material'sconductivity in Watts per meter-Kelvin, and ∇T is the temperaturegradient in Kelvins per meter.

The computing system determines (408) a thermal conductivity for eachlayer of the subsurface formation from the temperature gradient. Thethermal conductivity is the ratio of a rate of heat flow per unit areato a negative value of the temperature gradient.

The computing system generates (410) data representing relationshipsbetween thermal conductivity and elastic properties using rock physicstransforms (RPT) for the subsurface formation. Generally, this process,described in detail with respect to FIGS. 5A-5B, includes relatingthermal conductivity to seismic inversion values acquired from seismicmeasurements. For example, acoustic impedance is an elastic propertythat can be used to build a rock physics template. This template relatesacoustic impedance to thermal conductivity in a linear or non-linearrelationship.

The computing system then uses 3D seismic data (such as from seismicinversion) to generate (412) an areal map of thermal conductivity usingRPT. The areal map of thermal conductivity represents a 3D map that canbe superimposed on a volume in the subsurface formation (such as shownin FIG. 2 ).

The computing system estimates (414) source rock maturity from the arealmap of thermal conductivity. The source rock maturity and areal map ofthermal conductivity are each used, such as during hydrocarbonexploration, for drilling risk mitigation. For example, areas with oldersource rock (or more mature source rock) are more likely to yieldhydrocarbon deposits. When rock maturity exceeds a minimum thresholdrepresenting a required rock maturity, it is possible for that region toinclude hydrocarbons such as oil or gas. In some implementations,hydrocarbon exploration is performed (416) based on the determinedsource rock maturity for a region exceeding a threshold value. This mayindicate that hydrocarbons are present or could possibly present, andcan be used in combination with other data. Hydrocarbon exploration mayinclude performing a geologic survey in a region, drilling in a region,and so forth. For example, a well can be drilled in a subsurface regionwith increased rock maturity. In another example, a seismic survey canbe performed in the subsurface region with increased rock maturity, theseismic survey having increased sensor counts.

FIG. 5A illustrates a process 500 for determining relationship betweenseismic data and thermal conductivity in a subsurface region (such asthe 3D cube of FIG. 2 ). The process 500 can be performed by a dataprocessing system, such as the computing system 700 described inrelation to FIG. 7 , or the computer system 124 described in relation toFIG. 1 . The computing system executes a process 500 that uses each ofpetrophysics, rock physics and seismic inversion. The relationship canbe determined for process 400. The process 500 includes determining(502) a thermal conductivity for each layer in the subsurface formationfrom the temperature gradient. The computing device is configured toconduct (504) a pre-stack inversion from 3D seismic data. The computingsystem is configured to generate (508) an areal map of thermalconductivity using RPT-guided seismic inversion. In someimplementations, the process 500 includes performing (510) a cross-plotanalysis of thermal conductivity and pre-stack and post-stack well logattributes for rock physics conditioning of well logs. The cross-plotsare used to generate (512) a detailed rock physics template (RPT) thatrelates thermal conductivity to elastic attributes. To build the RPTs,the computing system is configured to gather production logs (such asfluid—structure interaction (FSI), PLT, and so forth) from sensors atthe subsurface formation. In some implementations, the log data arestored in a database and accessed by the computing system. The computingsystem is configured to perform a quality control (QC) analysis for eachlogging suite for reservoir temperature at the target reservoir in thesubsurface formation. The computing system determines a reservoirtemperature variation and range of temperature values in the subsurfaceregion. The computing system analyzes different open-hole logs availablein the subsurface region to compile the temperature data from differentlog headers and determine the range of reservoir temperature fromdifferent logging tools.

In some implementations, the computing device determines a thermalconductivity based on a set of values in a lookup table depending on thetype of lithology. Rather than these default values, the computingsystem uses seismic data to build rock physics transforms that relateelastic attributes to thermal conductivity. This is a more efficient wayfor mapping conductivity in a 3D sense based on hard data available (theseismic data). The computing device uses results of seismic inversion topropagate thermal conductivity values in a 3D model. For example, thecomputing system computes a thermal conductivity for each layer from thetemperature gradient and performs a pre-stack inversion from 3D seismicdata. The computing system generates an areal map of thermalconductivity using RPT-guided seismic inversion.

The horizontally and vertically spatial variant values are affected bythe type of facies predicted from seismic inversion and do not depend ona set of predefined values from tables that might not even correspond tothe type of geology in the subsurface formation of interest. Subsurfacetemperature distribution is a factor of how prospective a sedimentarybasin is for further exploration. A basin modeler provided with morerealistic values of thermal conductivity produces more accurate resultsof source rock maturity estimation. This provides insight into thelikely presence of gas or oil depending on whether the source rock hasreached a level of thermal maturity. Moreover, these predictions willprovide more robust estimates in frontier exploration where there islimited number of borehole data. In this way, the processes 400, 500performed by the computing system mitigate drilling risk.

FIG. 5B illustrates a process 550 for determining thermal conductivitywithin a three-dimensional cube (e.g., of FIG. 2 ) from seismic dataacquired in that three-dimensional cube. Seismic data 552 are invertedusing a deterministic inversion algorithm to generate 3D cubes ofelastic attributes. The process is comprised of three general steps. Ina first step, an elastic parameter contrast inversion occurs. Theelastic inversion provides an estimate of elastic parameters by the useof amplitude variations with angle data for interpretation of lithology,porosity and fluid type. In a second step, a contrast integration isperformed to convert the data to elastic parameters. IN a third step, anelastic parameter inversion is performed. An objective function isminimized to estimate optimum elastic parameters. An evaluation of theobjective function is conducted after every elastic parameter updateuntil a minimum is achieved or a maximum number of updates is reached.Rock physics templates 554 are developed that each relate thermalconductivity to elastic attributes of the subsurface region representedin the seismic inversion. The seismic inversion products of elasticattributes are transformed into a 3D map 556 of thermal conductivityusing the RPTs. The seismic inversion data, which is estimated fromseismic data, is used to determine acoustic impedance across the 3Darea. The rock physics template (RPT) is generated by relating acousticimpedance to thermal conductivity. Once the computing system hasestimated the acoustic impedance, the computing system uses the rockphysics relationship to propagate thermal conductivity values throughthe whole 3D area.

FIG. 6 illustrates a system 600 for determining source rock maturityfrom seismic data and a rock physics transform that relates seismicinversion elastic attributes of the seismic data to thermal conductivityin the subsurface. The system 600 includes borehole sensors 601 a andseismic sensors 601 b. The borehole sensors 601 a are configured toprovide data such as thermal data and other log data for storing in logdatabase 602. The seismic sensors 601 b provide acoustic data forstoring in database 604. A rock physics template generator 606 generatesthe RPTs 610 as previously described. The RPTs are sent to a thermalconductivity map generator 612 configured to generate the 3D thermalconductivity maps 614 as previously described.

FIG. 7 is a block diagram of an example computer system 700 used toprovide computational functionalities associated with describedalgorithms, methods, functions, processes, flows, and proceduresdescribed in the present specification, according to someimplementations of the present specification such as the data processingsystem 800 described in relation to FIG. 8 . The illustrated computer702 is intended to encompass any computing device such as a server, adesktop computer, a laptop/notebook computer, a wireless data port, asmart phone, a personal data assistant (PDA), a tablet computing device,or one or more processors within these devices, including physicalinstances, virtual instances, or both. The computer 702 can includeinput devices such as keypads, keyboards, and touch screens that canaccept user information. Also, the computer 702 can include outputdevices that can convey information associated with the operation of thecomputer 702. The information can include digital data, visual data,audio information, or a combination of information. The information canbe presented in a graphical user interface (UI) (or GUI).

The computer 702 can serve in a role as a client, a network component, aserver, a database, a persistency, or components of a computer systemfor performing the subject matter described in the presentspecification. The illustrated computer 702 is communicably coupled witha network 724. In some implementations, one or more components of thecomputer 702 can be configured to operate within different environments,including cloud-computing-based environments, local environments, globalenvironments, and combinations of environments.

At a top level, the computer 702 is an electronic computing deviceoperable to receive, transmit, process, store, and manage data andinformation associated with the described subject matter. According tosome implementations, the computer 702 can also include, or becommunicably coupled with, an application server, an email server, a webserver, a caching server, a streaming data server, or a combination ofservers.

The computer 702 can receive requests over network 724 from a clientapplication (for example, executing on another computer 702). Thecomputer 702 can respond to the received requests by processing thereceived requests using software applications. Requests can also be sentto the computer 702 from internal users (for example, from a commandconsole), external (or third) parties, automated applications, entities,individuals, systems, and computers.

Each of the components of the computer 702 can communicate using asystem bus 704. In some implementations, any or all of the components ofthe computer 702, including hardware or software components, caninterface with each other or the interface 706 (or a combination ofboth) over the system bus 704. Interfaces can use an applicationprogramming interface (API) 714, a service layer 716, or a combinationof the API 714 and service layer 716. The API 714 can includespecifications for routines, data structures, and object classes. TheAPI 714 can be either computer-language independent or dependent. TheAPI 714 can refer to a complete interface, a single function, or a setof APIs.

The service layer 716 can provide software services to the computer 702and other components (whether illustrated or not) that are communicablycoupled to the computer 702. The functionality of the computer 702 canbe accessible for all service consumers using this service layer.Software services, such as those provided by the service layer 716, canprovide reusable, defined functionalities through a defined interface.For example, the interface can be software written in JAVA, C++, or alanguage providing data in extensible markup language (XML) format.While illustrated as an integrated component of the computer 702, inalternative implementations, the API 714 or the service layer 716 can bestand-alone components in relation to other components of the computer702 and other components communicably coupled to the computer 702.Moreover, any or all parts of the API 714 or the service layer 716 canbe implemented as child or sub-modules of another software module,enterprise application, or hardware module without departing from thescope of the present specification.

The computer 702 includes an interface 706. Although illustrated as asingle interface 706 in FIG. 7 , two or more interfaces 706 can be usedaccording to particular needs, desires, or particular implementations ofthe computer 702 and the described functionality. The interface 706 canbe used by the computer 702 for communicating with other systems thatare connected to the network 724 (whether illustrated or not) in adistributed environment. Generally, the interface 706 can include, or beimplemented using, logic encoded in software or hardware (or acombination of software and hardware) operable to communicate with thenetwork 724. More specifically, the interface 706 can include softwaresupporting one or more communication protocols associated withcommunications. As such, the network 724 or the interface's hardware canbe operable to communicate physical signals within and outside of theillustrated computer 702.

The computer 702 includes a processor 708. Although illustrated as asingle processor 708 in FIG. 7 , two or more processors 708 can be usedaccording to particular needs, desires, or particular implementations ofthe computer 702 and the described functionality. Generally, theprocessor 708 can execute instructions and can manipulate data toperform the operations of the computer 702, including operations usingalgorithms, methods, functions, processes, flows, and procedures asdescribed in the present specification.

The computer 702 also includes a database 720 that can hold data for thecomputer 702 (such as log data 722 a, 722 b, 722 c) and other componentsconnected to the network 724 (whether illustrated or not). For example,database 720 can be an in-memory, conventional, or a database storingdata consistent with the present specification. In some implementations,database 720 can be a combination of two or more different databasetypes (for example, hybrid in-memory and conventional databases)according to particular needs, desires, or particular implementations ofthe computer 702 and the described functionality. Although illustratedas a single database 720 in FIG. 7 , two or more databases (of the same,different, or combination of types) can be used according to particularneeds, desires, or particular implementations of the computer 702 andthe described functionality. While database 720 is illustrated as aninternal component of the computer 702, in alternative implementations,database 720 can be external to the computer 702.

The computer 702 also includes a memory 710 that can hold data for thecomputer 702 or a combination of components connected to the network 724(whether illustrated or not). Memory 710 can store any data consistentwith the present specification. In some implementations, memory 710 canbe a combination of two or more different types of memory (for example,a combination of semiconductor and magnetic storage) according toparticular needs, desires, or particular implementations of the computer702 and the described functionality. Although illustrated as a singlememory 710 in FIG. 7 , two or more memories 710 (of the same, different,or combination of types) can be used according to particular needs,desires, or particular implementations of the computer 702 and thedescribed functionality. While memory 710 is illustrated as an internalcomponent of the computer 702, in alternative implementations, memory710 can be external to the computer 702.

The application 712 can be an algorithmic software engine providingfunctionality according to particular needs, desires, or particularimplementations of the computer 702 and the described functionality. Forexample, application 712 can serve as one or more components, modules,or applications. Further, although illustrated as a single application712, the application 712 can be implemented as multiple applications 708on the computer 702. In addition, although illustrated as internal tothe computer 702, in alternative implementations, the application 712can be external to the computer 702.

The computer 702 can also include a power supply 718. The power supply718 can include a rechargeable or non-rechargeable battery that can beconfigured to be either user- or non-user-replaceable. In someimplementations, the power supply 718 can include power-conversion andmanagement circuits, including recharging, standby, and power managementfunctionalities. In some implementations, the power-supply 718 caninclude a power plug to allow the computer 702 to be plugged into a wallsocket or a power source to, for example, power the computer 702 orrecharge a rechargeable battery.

There can be any number of computers 702 associated with, or externalto, a computer system containing computer 702, with each computer 702communicating over network 724. Further, the terms “client,” “user,” andother appropriate terminology can be used interchangeably, asappropriate, without departing from the scope of the presentspecification. Moreover, the present specification contemplates thatmany users can use one computer 702 and one user can use multiplecomputers 702.

Implementations of the subject matter and the functional operationsdescribed in this specification can be implemented in digital electroniccircuitry, in tangibly embodied computer software or firmware, incomputer hardware, including the structures disclosed in thisspecification and their structural equivalents, or in combinations ofone or more of them. Software implementations of the described subjectmatter can be implemented as one or more computer programs. Eachcomputer program can include one or more modules of computer programinstructions encoded on a tangible, non-transitory, computer-readablecomputer-storage medium for execution by, or to control the operationof, data processing apparatus. Alternatively, or additionally, theprogram instructions can be encoded in/on an artificially generatedpropagated signal. The example, the signal can be a machine-generatedelectrical, optical, or electromagnetic signal that is generated toencode information for transmission to suitable receiver apparatus forexecution by a data processing apparatus. The computer-storage mediumcan be a machine-readable storage device, a machine-readable storagesubstrate, a random or serial access memory device, or a combination ofcomputer-storage mediums.

The terms “data processing apparatus,” “computer,” and “electroniccomputer device” (or equivalent as understood by one of ordinary skillin the art) refer to data processing hardware. For example, a dataprocessing apparatus can encompass all kinds of apparatus, devices, andmachines for processing data, including by way of example, aprogrammable processor, a computer, or multiple processors or computers.The apparatus can also include special purpose logic circuitryincluding, for example, a central processing unit (CPU), a fieldprogrammable gate array (FPGA), or an application specific integratedcircuit (ASIC). In some implementations, the data processing apparatusor special purpose logic circuitry (or a combination of the dataprocessing apparatus or special purpose logic circuitry) can behardware- or software-based (or a combination of both hardware- andsoftware-based). The apparatus can optionally include code that createsan execution environment for computer programs, for example, code thatconstitutes processor firmware, a protocol stack, a database managementsystem, an operating system, or a combination of execution environments.The present specification contemplates the use of data processingapparatuses with or without conventional operating systems, for example,LINUX, UNIX, WINDOWS, MAC OS, ANDROID, or IOS.

A computer program, which can also be referred to or described as aprogram, software, a software application, a module, a software module,a script, or code, can be written in any form of programming language.Programming languages can include, for example, compiled languages,interpreted languages, declarative languages, or procedural languages.Programs can be deployed in any form, including as stand-alone programs,modules, components, subroutines, or units for use in a computingenvironment. A computer program can, but need not, correspond to a filein a file system. A program can be stored in a portion of a file thatholds other programs or data, for example, one or more scripts stored ina markup language document, in a single file dedicated to the program inquestion, or in multiple coordinated files storing one or more modules,sub programs, or portions of code. A computer program can be deployedfor execution on one computer or on multiple computers that are located,for example, at one site or distributed across multiple sites that areinterconnected by a communication network. While portions of theprograms illustrated in the various figures may be shown as individualmodules that implement the various features and functionality throughvarious objects, methods, or processes, the programs can instead includea number of sub-modules, third-party services, components, andlibraries. Conversely, the features and functionality of variouscomponents can be combined into single components as appropriate.Thresholds used to make computational determinations can be statically,dynamically, or both statically and dynamically determined.

The methods, processes, or logic flows described in this specificationcan be performed by one or more programmable computers executing one ormore computer programs to perform functions by operating on input dataand generating output. The methods, processes, or logic flows can alsobe performed by, and apparatus can also be implemented as, specialpurpose logic circuitry, for example, a CPU, an FPGA, or an ASIC.

Computers suitable for the execution of a computer program can be basedon one or more of general and special purpose microprocessors and otherkinds of CPUs. The elements of a computer are a CPU for performing orexecuting instructions and one or more memory devices for storinginstructions and data. Generally, a CPU can receive instructions anddata from (and write data to) a memory. A computer can also include, orbe operatively coupled to, one or more mass storage devices for storingdata. In some implementations, a computer can receive data from, andtransfer data to, the mass storage devices including, for example,magnetic, magneto optical disks, or optical disks. Moreover, a computercan be embedded in another device, for example, a mobile telephone, apersonal digital assistant (PDA), a mobile audio or video player, a gameconsole, a global positioning system (GPS) receiver, or a portablestorage device such as a universal serial bus (USB) flash drive.

Computer readable media (transitory or non-transitory, as appropriate)suitable for storing computer program instructions and data can includeall forms of permanent/non-permanent and volatile/non-volatile memory,media, and memory devices. Computer readable media can include, forexample, semiconductor memory devices such as random access memory(RAM), read only memory (ROM), phase change memory (PRAM), static randomaccess memory (SRAM), dynamic random access memory (DRAM), erasableprogrammable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), and flash memory devices.Computer readable media can also include, for example, magnetic devicessuch as tape, cartridges, cassettes, and internal/removable disks.Computer readable media can also include magneto optical disks andoptical memory devices and technologies including, for example, digitalvideo disc (DVD), CD ROM, DVD+/-R, DVD-RAM, DVD-ROM, HD-DVD, and BLURAY.The memory can store various objects or data, including caches, classes,frameworks, applications, modules, backup data, jobs, web pages, webpage templates, data structures, database tables, repositories, anddynamic information. Types of objects and data stored in memory caninclude parameters, variables, algorithms, instructions, rules,constraints, and references. Additionally, the memory can include logs,policies, security or access data, and reporting files. The processorand the memory can be supplemented by, or incorporated in, specialpurpose logic circuitry.

Implementations of the subject matter described in the presentspecification can be implemented on a computer having a display devicefor providing interaction with a user, including displaying informationto (and receiving input from) the user. Types of display devices caninclude, for example, a cathode ray tube (CRT), a liquid crystal display(LCD), a light-emitting diode (LED), and a plasma monitor. Displaydevices can include a keyboard and pointing devices including, forexample, a mouse, a trackball, or a trackpad. User input can also beprovided to the computer through the use of a touchscreen, such as atablet computer surface with pressure sensitivity or a multi-touchscreen using capacitive or electric sensing. Other kinds of devices canbe used to provide for interaction with a user, including to receiveuser feedback including, for example, sensory feedback including visualfeedback, auditory feedback, or tactile feedback. Input from the usercan be received in the form of acoustic, speech, or tactile input. Inaddition, a computer can interact with a user by sending documents to,and receiving documents from, a device that is used by the user. Forexample, the computer can send web pages to a web browser on a user'sclient device in response to requests received from the web browser.

The term “graphical user interface,” or “GUI,” can be used in thesingular or the plural to describe one or more graphical user interfacesand each of the displays of a particular graphical user interface.Therefore, a GUI can represent any graphical user interface, including,but not limited to, a web browser, a touch screen, or a command lineinterface (CLI) that processes information and efficiently presents theinformation results to the user. In general, a GUI can include aplurality of user interface (UI) elements, some or all associated with aweb browser, such as interactive fields, pull-down lists, and buttons.These and other UI elements can be related to or represent the functionsof the web browser.

Implementations of the subject matter described in this specificationcan be implemented in a computing system that includes a back endcomponent, for example, as a data server, or that includes a middlewarecomponent, for example, an application server. Moreover, the computingsystem can include a front-end component, for example, a client computerhaving one or both of a graphical user interface or a Web browserthrough which a user can interact with the computer. The components ofthe system can be interconnected by any form or medium of wireline orwireless digital data communication (or a combination of datacommunication) in a communication network. Examples of communicationnetworks include a local area network (LAN), a radio access network(RAN), a metropolitan area network (MAN), a wide area network (WAN),Worldwide Interoperability for Microwave Access (WIMAX), a wirelesslocal area network (WLAN) (for example, using 402.11 a/b/g/n or 402.20or a combination of protocols), all or a portion of the Internet, or anyother communication system or systems at one or more locations (or acombination of communication networks). The network can communicatewith, for example, Internet Protocol (IP) packets, frame relay frames,asynchronous transfer mode (ATM) cells, voice, video, data, or acombination of communication types between network addresses.

The computing system can include clients and servers. A client andserver can generally be remote from each other and can typicallyinteract through a communication network. The relationship of client andserver can arise by virtue of computer programs running on therespective computers and having a client-server relationship.

Cluster file systems can be any file system type accessible frommultiple servers for read and update. Locking or consistency trackingmay not be necessary since the locking of exchange file system can bedone at application layer. Furthermore, Unicode data files can bedifferent from non-Unicode data files.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of what may beclaimed, but rather as descriptions of features that may be specific toparticular implementations. Certain features that are described in thisspecification in the context of separate implementations can also beimplemented, in combination, in a single implementation. Conversely,various features that are described in the context of a singleimplementation can also be implemented in multiple implementations,separately, or in any suitable sub-combination. Moreover, althoughpreviously described features may be described as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can, in some cases, be excised from thecombination, and the claimed combination may be directed to asub-combination or variation of a sub-combination.

Particular implementations of the subject matter have been described.Other implementations, alterations, and permutations of the describedimplementations are within the scope of the following claims as will beapparent to those skilled in the art. While operations are depicted inthe drawings or claims in a particular order, this should not beunderstood as requiring that such operations be performed in theparticular order shown or in sequential order, or that all illustratedoperations be performed (some operations may be considered optional), toachieve desirable results. In certain circumstances, multitasking orparallel processing (or a combination of multitasking and parallelprocessing) may be advantageous and performed as deemed appropriate.

Moreover, the separation or integration of various system modules andcomponents in the previously described implementations should not beunderstood as requiring such separation or integration in allimplementations, and it should be understood that the described programcomponents and systems can generally be integrated together in a singlesoftware product or packaged into multiple software products.

Furthermore, any claimed implementation is considered to be applicableto at least a computer-implemented method; a non-transitory,computer-readable medium storing computer-readable instructions toperform the computer-implemented method; and a computer systemcomprising a computer memory interoperably coupled with a hardwareprocessor configured to perform the computer-implemented method or theinstructions stored on the non-transitory, computer-readable medium.

While this specification contains many details, these should not beconstrued as limitations on the scope of what may be claimed, but ratheras descriptions of features specific to particular examples. Certainfeatures that are described in this specification in the context ofseparate implementations can also be combined. Conversely, variousfeatures that are described in the context of a single implementationcan also be implemented in multiple embodiments separately or in anysuitable sub-combination.

A number of embodiments have been described. Nevertheless, it will beunderstood that various modifications may be made without departing fromthe scope of the data processing system described herein. Accordingly,other embodiments are within the scope of the following claims.

What is claimed is:
 1. A method for modeling basin geology in asubsurface region, the method comprising: receiving seismic datarepresenting acoustic signals that are reflected from regions of thesubsurface region; receiving potential fields data comprising potentialfield values that are mapped to locations in the subsurface region;determining a relationship between the seismic data and the potentialfield values for each of the locations in the subsurface region;generating, based on the relationship for each of the locations, athree-dimensional (3D) map of thermal conductivity in the subsurfaceregion; and based on the 3D map of thermal conductivity, identifying atleast one area comprising source rock having a threshold maturity, thethreshold maturity being indicative of potential hydrocarbons in thesubsurface region.
 2. The method of claim 1, further comprising:determining a temperature curve in a zone of interest based on thepotential field values; and estimating a temperature gradient for eachlayer of a subsurface formation in the subsurface region from thetemperature curve, wherein the relationship between the seismic data andthe potential field values for each of the locations in the subsurfaceregion is based on the temperature gradient for a respective layer ofsubsurface formation.
 3. The method of claim 2, further comprising:determining a thermal conductivity for each layer of the subsurfaceformation from the temperature gradient for the respective layer of thesubsurface formation.
 4. The method of claim 1, further comprising:performing a seismic inversion on the seismic data; and generating,based on the seismic inversion, values for one or more elasticproperties of the subsurface region, wherein the relationship betweenthe seismic data and the potential field values for each of thelocations in the subsurface region comprises a relationship between thethermal conductivity of the subsurface region and the one or moreelastic properties.
 5. The method of claim 4, wherein the one or moreelastic properties comprise lithology, porosity, water saturation,permeability and density.
 6. The method of claim 4, further comprising:determining, based on the seismic inversion, an acoustic impedanceacross the subsurface region; generating a rock physics template (RPT)based on relating the acoustic impedance to thermal conductivity in thesubsurface region; and based on the RPT, propagate thermal conductivityvalues through the subsurface region to form the 3D map of thermalconductivity.
 7. The method of claim 1, wherein the potential fieldsdata comprises production logging tool (PLT) data, downhole seismictesting (DST) data, bottom hole temperature (BHT) log data, or acombination thereof.
 8. A system for modeling basin geology in asubsurface region, the system comprising: at least one processor; and atleast one memory storing instructions that, when executed by the atleast one processor, cause the at least one processor to performoperations comprising: receiving seismic data representing acousticsignals that are reflected from regions of the subsurface region;receiving potential fields data comprising potential field values thatare mapped to locations in the subsurface region; determining arelationship between the seismic data and the potential field values foreach of the locations in the subsurface region; and generating, based onthe relationship for each of the locations, a three-dimensional (3D) mapof thermal conductivity in the subsurface region; and based on the 3Dmap of thermal conductivity, identifying at least one area comprisingsource rock having a threshold maturity, the threshold maturity beingindicative of potential hydrocarbons in the subsurface region.
 9. Thesystem of claim 8, further comprising: determining a temperature curvein a zone of interest based on the potential field values; estimating atemperature gradient for each layer of a subsurface formation in thesubsurface region from the temperature curve, wherein the relationshipbetween the seismic data and the potential field values for each of thelocations in the subsurface region is based on the temperature gradientfor a respective layer of subsurface formation.
 10. The system of claim9, further comprising: determining a thermal conductivity for each layerof the subsurface formation from the temperature gradient for therespective layer of the subsurface formation.
 11. The system of claim 8,further comprising: performing a seismic inversion on the seismic data;and generating, based on the seismic inversion, values for one or moreelastic properties of the subsurface region, wherein the relationshipbetween the seismic data and the potential field values for each of thelocations in the subsurface region comprises a relationship between thethermal conductivity of the subsurface region and the one or moreelastic properties.
 12. The system of claim 11, wherein the one or moreelastic properties comprise lithology, porosity, water saturation,permeability and density.
 13. The system of claim 11, furthercomprising: determining, based on the seismic inversion, an acousticimpedance across the subsurface region; generating a rock physicstemplate (RPT) based on relating the acoustic impedance to thermalconductivity in the subsurface region; and based on the RPT, propagatethermal conductivity values through the subsurface region to form the 3Dmap of thermal conductivity.
 14. The system of claim 8, wherein thepotential fields data comprises production logging tool (PLT) data,downhole seismic testing (DST) data, bottom hole temperature (BHT) logdata, or a combination thereof.
 15. One or more non-transitorycomputer-readable media storing instructions for modeling basin geologyin a subsurface region, the instructions, when executed by at least oneprocessor, being configured to cause the at least one processor toperform operations comprising: receiving seismic data representingacoustic signals that are reflected from regions of the subsurfaceregion; receiving potential fields data comprising potential fieldvalues that are mapped to locations in the subsurface region;determining a relationship between the seismic data and the potentialfield values for each of the locations in the subsurface region;generating, based on the relationship for each of the locations, athree-dimensional (3D) map of thermal conductivity in the subsurfaceregion; and based on the 3D map of thermal conductivity, identifying atleast one area comprising source rock having a threshold maturity, thethreshold maturity being indicative of potential hydrocarbons in thesubsurface region.
 16. The one or more non-transitory computer-readablemedia of claim 15, further comprising: determining a temperature curvein a zone of interest based on the potential field values; andestimating a temperature gradient for each layer of a subsurfaceformation in the subsurface region from the temperature curve, whereinthe relationship between the seismic data and the potential field valuesfor each of the locations in the subsurface region is based on thetemperature gradient for a respective layer of subsurface formation. 17.The one or more non-transitory computer-readable media of claim 16,further comprising: determining a thermal conductivity for each layer ofthe subsurface formation from the temperature gradient for therespective layer of the subsurface formation.
 18. The one or morenon-transitory computer-readable media of claim 15, further comprising:performing a seismic inversion on the seismic data; and generating,based on the seismic inversion, values for one or more elasticproperties of the subsurface region, wherein the relationship betweenthe seismic data and the potential field values for each of thelocations in the subsurface region comprises a relationship between thethermal conductivity of the subsurface region and the one or moreelastic properties.
 19. The one or more non-transitory computer-readablemedia of claim 18, wherein the one or more elastic properties compriselithology, porosity, water saturation, permeability and density.
 20. Theone or more non-transitory computer-readable media of claim 18, furthercomprising: determining, based on the seismic inversion, an acousticimpedance across the subsurface region; generating a rock physicstemplate (RPT) based on relating the acoustic impedance to thermalconductivity in the subsurface region; and based on the RPT, propagatethermal conductivity values through the subsurface region to form the 3Dmap of thermal conductivity.